Wednesday, January 19, 2011

I'm switching teams this month. I'll be working out of the DRW New York City office in the near future. I've been with my current team for almost 2 years, and I've learned plenty of valuable lessons. I'm not sure if the most valuable take-away centers around Compatible Opinions on Software; however, it's definitely the take-away that seems to remain in the fore-front of my memories.

The guys I work with in the DRW CT office are truly talented. Each individual on my team is someone I would consider to be a great software developer. As a result, we don't tend to have discussions on issues that where one answer is clearly right and another is clearly wrong. Instead, we often find ourselves in situations where one or more individuals have differing opinions and a "correct" answer cannot be found. Despite everyone being an excellent developer, several members of the team have incompatible opinions on software.

Here's the net result: Quality software. We aren't talking about a problem that caused projects to fail. On the contrary, our customer is very happy and the team is largely happy with the product we deliver. Unfortunately, as a teammate puts it, I seem to be immune to complacency. Even though things are good, I wont be satisfied until they are great. And, I feel that incompatible opinions on software is our largest production thief.

In general, it seems that anything can cause incompatible opinions. However, to make clear what I'm talking about, here is a short sample list of incompatible opinions I've run into:

powerful language vs. powerful IDE

monolithic application vs many small components

what level of duplication is acceptable, and why

specialization vs generalization

team size

For the majority of my career I've believed - if I work on a team of excellent developers everything will simply fall into place. I now believe that being technically excellent is only the first requirement. I also believe that you can have two technically excellent people who have vastly different opinions on the most effective way to deliver software.

Two developers who prefer a powerful language may or may not be more productive than two developers who prefer a powerful IDE. However, both of the previous pairs will be more productive than one developer who prefers a powerful language teamed up with one developer who prefers a powerful IDE.

I'm not talking about people who love IntelliJ but are perfectly willing to learn emacs. If you're open to working in emacs then you have a compatible opinion with an emacs zealot. I'm talking about people who have tried the opposing point of view and still have a strong opinion that it's not the best way to deliver software. Putting those people on the same team only ensures a never-ending battle about why things aren't as good as they could be.

Of course, having differing views can be a good thing. Diversity ensures all kinds of benefits. I'm not suggesting that you rid yourself of all diversity. However, for the sake of productivity, I suggest that you do your best to avoid working with someone who has both an opposing view and is as inflexible as you are on the subject. The more central the subject is to the project, the more likely it is that productivity will be lost.

Let's get back to a concrete example. For me personally, I strongly prefer Clojure to Java. That's not to say that Java doesn't have it's place, it does. But, if both languages are valid choices, I'll always opt for Clojure. Given that opinion, I'm more effective if I'm working with someone else that prefers Clojure to Java. The language decision is important to me, very central to the project, and likely to be an ongoing issue.

Building off of the previous example, I don't personally care if we use IntelliJ or Emacs. As long as the IDE or editor choice is a reasonable one, I'm happy to use whatever someone else on the team prefers. I welcome diversity on compatible opinions. In fact, it's likely that diversity on compatible opinions will lead to greater productivity as better solutions are found.

So what can you do? The obvious answers: I've started making a list of opinions that I'm less flexible about. If I'm interviewing for a teammate, I'll ask questions to ensure we have compatible opinions on software. If I'm interviewing for a new job, I'll ask the interviewers about their opinions on software. To a certain extent I've always asked these questions, but I think I'll have better results if I identify which of my opinions I'm less flexible about and focus a bit more on ensuring that there aren't any conflicts.

If you already have a team in place and aren't looking to leave it's a bit harder to resolve incompatible opinions on software. Depending on the size of your team, you may be able to split the team up into smaller groups of people who have compatible opinions on software. Obviously, this will depend on having enough people to allow for a split, and having software that can be logically divided. If you can pull this off, you also get the nice benefit of seeing the results from doing things differently, without having to change your approach until it makes sense for you.

At this point I've worked on three different teams that achieved a level of productivity greater than that of any other teams I've been a part of. Of course, there are several factors that contributed to the success of all three teams. However, all three teams had compatible opinions on software, and avoided an entire class of problems that erode productivity. You'd have a hard time convincing me that those two things weren't somehow related.

Wednesday, January 12, 2011

Recently, I wanted to add some specific behavior to my Clojure application when any uncaught exception occurred. This is easy enough by calling the setDefaultUncaughtExceptionHandler (sDUEH); however, it's worth noting that the sDUEH takes a Thread.UncaughtExceptionHandler argument. Clojure has no problem giving you access to inner classes, but the syntax is slightly different: Outer$Inner in Clojure is the same as Outer.Inner in Java.

This works without issue, because java.lang is (generally) available in Clojure.

If you want access to an inner class that is not in java.lang you'll want to import it directly. For example, the following REPL session shows how you can access the ThreadPoolExecutor.DiscardPolicy class.

Tuesday, January 11, 2011

The fnil function was added to Clojure in version 1.2. The fnil function is a great addition that allows you to write code that works for all cases where an argument isn't nil, and handle the case where it is nil.

From the documentation: The fnil function takes a function f, and returns a function that calls f, replacing a nil first argument to f with a supplied value.

As you can see, the + function throws an exception if an argument is nil; however, we were easily able to create our own new+ function that handles the first argument being nil.

In isolation, it might be hard to see how this is valuable. However, once combined with high order functions it's easy to see the benefit.

Several months ago I wrote about composing functions and used the update-in function as my final example of what I thought was the best implementation. What I didn't address in the blog entry was how to handle the first update.

As you can see from the following code, the first update will fail if a default value isn't populated.

While the above code works perfectly well, it's obviously not nearly as nice to read as the example that doesn't need to concern itself with nil.

However, Clojure 1.2 is now production ready and the fnil function is available. As a result, you can now write the following version of the update-score function that is obviously preferable to the version that uses the or function.

I'll admit that fnil wasn't my favorite function when I first found it; however, it's become indispensable. Looking through my code I find (fnil + 0) and (fnil - 0) a few times, and I definitely prefer those to the versions that use the or function.

Thursday, January 06, 2011

Clojure provides a few different options for creating functions inline: fn (or the #() reader macro), partial, and comp. When I first got started with Clojure I found I could do everything with fn and #(); and that's a good place to start. However, as I produced more Clojure code I found there were also opportunities to use both partial and comp to create more concise code.

The following examples are contrived. They will show how partial and comp can be used; however, they aren't great examples of when they should be used. As always, context is important, and you'll need to decide when (or if) you want to use either function.

The partial function takes a function and fewer than the normal arguments to the function, and returns a function that takes a variable number of additional args. When called, the returned function calls the function with the specified args and any additional args.

The partial function can often be used as an alternative to fn or #(). The following example shows how you can use either #() or partial to multiply a list of integers by .01 (convert pennies to dollars).

In a straightforward example, such as the one above, it's really up to you which you'd prefer. However, if you want to specify a predicate that takes a variable number of args then the case for partial starts to become a bit more noticeable.

I haven't come across an example yet that made me think: The partial function is definitely the right choice here! However, I have passed around a few functions that had several arguments and found I prefer (partial f arg1) to #(f arg1 %1 %2 %3) and #(apply f arg1 %&).

The comp function takes a variable number of functions and returns a function that is the composition of those functions. The returned function takes a variable number of args, applies the rightmost of functions to the args, the next function (right-to-left) to the result, etc.

In a previous blog entry I used comp to return the values from a map given a list of keys. Below you can find the same example that shows the definition and usage.

As you can see from the example, comp creates a function that takes a map and a sequence of keys, calls select-keys with that sequence, then calls vals with the result of calling select-keys. As the documentation specifies, the functions are called from right to left.

In general I find myself executing some functions directly with some data. In that case I generally use the -> macro. For example, if I already have a map and I want a list of the keys I'm probably going to write code similar to what's found below.

user=> (-> {:a 1 :b 2} (select-keys [:a]) vals)
(1)

However, there are times when you need a function that is the composition of a few other functions. For example, taking a list of numbers, converting them to strings, and then converting them to keywords.

user=> (map (comp keyword str) [1 2])
(:1 :2)

The same thing can be done with #(), as the example below shows.

user=> (map #(keyword (str %1)) [1 2])
(:1 :2)

While the code above works perfectly well, I definitely prefer the version that uses the comp function.

Like so many other functions in Clojure, you can get by without partial and comp. However, I find my code more readable and maintainable when I use tools specifically designed to handle my current problem.

Wednesday, January 05, 2011

Clojure provides the get and get-in functions for returning values from a map and the select-keys function for returning a new map of only the specified keys. Clojure doesn't provide a function that returns a list of values; however, it's very easy to create such a function (which I call select-values). Once you have the ability to select-values it becomes very easy to create a function that applies a function to the selected values (which I call apply-values).

The select-keys function returns a map containing only the entries of the specified keys. The following (pasted) REPL session shows a few different select-keys behaviors.

The select-keys function is helpful in many occassions; however, sometimes you only care about selecting the values of certain keys in a map. A simple solution is to call select-keys and then vals. Below you can find the results of applying this idea.

The select-values implementation from above may be sufficient for what you are doing, but there are two things worth noticing: in cases where you might be expecting an empty list you are seeing nil; and, the values are not in the same order that the keys were specified in. Given that (standard) maps are unsorted, you can't be sure of the ordering the values.

(side-note: If you are concerned with microseconds, it's also been reported that select-keys is a bit slow/garbage heavy.)

An alternative definition of select-values uses the reduce function and pulls the values by key and incrementally builds the (vector) result.

The new select-values function returns the values in order and returns an empty vector in the cases where previous examples returned nil, but we have a new problem: Keys specified that don't exist in the map are now included in the vector as nil. This issue is easily addressed by adding a call to the remove function.

There is no "correct" implementation for select-values. If you don't care about ordering and nil is a reasonable return value: the first implementation is the correct choice due to it's concise definition. If you do care about ordering and performance: the second implementation might be the right choice. If you want something that follows the principle of least surprise: the third implementation is probably the right choice. You'll have to decide what's best for your context. In fact, here's a few more implementations that might be better based on your context.

Pulling values from a map is helpful, but it's generally not the end goal. If you find yourself pulling values from a map, it's likely that you're going to want to apply a function to the extracted values. With that in mind, I generally define an apply-values function that returns the result of applying a function to the values returned from specified keys.

A good example of this is returning the total for a line item represented as a map. Given a map that specifies a line item costing $5 and having a quantity of 4, you can use (* price quantity) to determine the total price for the line item.

Using our previously defined select-values function we can do the work ourselves, as the example below shows.

The example above works perfectly well; however, applying a function to the values of a map seems like a fairly generic operation that can easily be extracted to it's own function (the apply-values function). The example below shows the definition and usage of my definition of apply-values.

I find select-keys, select-values, & apply-values to be helpful when writing Clojure applications. If you find you need these functions, feel free to use them in your own code. However, you'll probably want to check the comments - I'm sure someone with more Clojure experience than I have will provide superior implementations.